Evolution of Attention Mechanisms and Transformer Models in AI

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The chapter explores the evolution of attention mechanisms in artificial intelligence, from the origins in machine translation to the development of self-attention and transformer models like GPT-3. It discusses the importance of large datasets, increasing parameters, and the integration of attention for learning higher order dependencies. The conversation also covers advancements in AI models like BERT, the significance of reinforcement learning from human feedback, and the potential of using explanations for outputs to enhance reasoning abilities in intelligent agents.

Arvind Srinivas is CEO of Perplexity, a company that aims to revolutionize how we humans find answers to questions on the Internet. Please support this podcast by checking out our sponsors:
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Transcript: https://lexfridman.com/aravind-srinivas-transcript

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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(10:52) – How Perplexity works
(18:48) – How Google works
(41:16) – Larry Page and Sergey Brin
(55:50) – Jeff Bezos
(59:18) – Elon Musk
(1:01:36) – Jensen Huang
(1:04:53) – Mark Zuckerberg
(1:06:21) – Yann LeCun
(1:13:07) – Breakthroughs in AI
(1:29:05) – Curiosity
(1:35:22) – $1 trillion dollar question
(1:50:13) – Perplexity origin story
(2:05:25) – RAG
(2:27:43) – 1 million H100 GPUs
(2:30:15) – Advice for startups
(2:42:52) – Future of search
(3:00:29) – Future of AI